A multi-sensor fusion high-altitude operation hook three-dimensional posture monitoring and early warning method

Abstract

The application discloses a multi-sensor fusion aerial operation hook three-dimensional posture monitoring and early warning method, and relates to the technical field of aerial operation safety monitoring. The method comprises the following steps: acquiring a locking state signal of a hook of a speed difference self-locking device, a load bearing connection state signal and real-time inertial measurement data; performing Kalman filtering on the real-time inertial measurement data to generate hook three-dimensional posture data; performing first-level risk analysis on the hook three-dimensional posture based on the locking state signal to generate a first priority early warning instruction representing a hook locking failure; performing second-level risk analysis on the hook three-dimensional posture according to the load bearing connection state signal to generate a second priority early warning instruction representing a hook hooking failure; analyzing the change characteristics of the hook three-dimensional posture data, and performing third-level risk analysis on the hook three-dimensional posture according to the change characteristics to generate a third priority early warning instruction representing an abnormal spatial position of a hook anchor point. The application improves the accuracy of aerial operation hook three-dimensional posture monitoring and early warning.

Description

Technical Field

[0001] This invention relates to the field of high-altitude operation safety monitoring technology, and in particular to a multi-sensor fusion method for monitoring and early warning of the three-dimensional attitude of a high-altitude operation hook. Background Technology

[0002] The self-locking device is one of the most important personal fall protection devices for workers at heights, and it is widely used in high-altitude operations such as high-voltage transmission line operation and maintenance, building construction, and bridge maintenance. Existing self-locking devices mainly rely on purely mechanical structures to achieve fall protection, and their safety depends entirely on the standardized operation of the workers and the on-site visual inspection by ground supervisors.

[0003] In existing technologies, the locking state of the hook in a speed-differential self-locking device is achieved solely through the cooperation of a mechanical spring and a locking tongue. Ground monitoring personnel cannot determine whether the locking tongue has fully reached the locking endpoint. When the internal transmission mechanism of the hook wears down, the spring breaks due to fatigue, or foreign objects become stuck, the hook may experience a false closure while remaining open, posing a safety hazard. That is, the locking tongue only rebounds to a partial travel position and does not fully engage with the hook's locking groove. This mechanical fault cannot be detected by visual inspection and can only be exposed after the hook is released under force, making it highly concealed. Furthermore, strong winds in the field can cause equipment swaying, and the shaking of personnel during climbing can cause significant fluctuations in sensor data, resulting in low accuracy of the hook's three-dimensional attitude monitoring and early warning. Summary of the Invention

[0004] This invention provides a multi-sensor fusion method for three-dimensional attitude monitoring and early warning of aerial work hooks, the main purpose of which is to solve the problem of low accuracy in three-dimensional attitude monitoring and early warning of aerial work hooks.

[0005] To achieve the above objectives, the present invention provides a multi-sensor fusion method for three-dimensional attitude monitoring and early warning of aerial work hooks, comprising:

[0006] In response to the mechanical operation of the hook of the speed-differential self-locking device, the locking status signal, load-bearing connection status signal and real-time inertial measurement data of the hook of the speed-differential self-locking device are acquired simultaneously.

[0007] The real-time inertial measurement data is subjected to Kalman filtering, and the filtered data is compensated for drift online to generate three-dimensional attitude data of the hook.

[0008] Based on the locking state signal, a first-level risk analysis is performed on the three-dimensional attitude of the hook. If the analysis result meets the preset first risk condition, a first-priority early warning instruction representing the hook locking failure is generated.

[0009] Under the premise that the locking status signal indicates that the hook lock tongue is in a fully locked state, a second-level risk analysis is performed on the three-dimensional attitude of the hook according to the load-bearing connection status signal. If the analysis result meets the preset second risk condition, a second priority warning instruction characterizing the hook connection failure is generated.

[0010] Under the premise that the locking status signal indicates that the hook latch is in a fully locked state and the load-bearing connection status signal indicates that the hook is in a valid connection state, the change characteristics of the hook's three-dimensional attitude data are analyzed, and a third-level risk analysis of the hook's three-dimensional attitude is performed based on the change characteristics. If the analysis result meets the preset third risk conditions, a third-priority early warning instruction characterizing the abnormal spatial position of the hook anchor point is generated.

[0011] This invention, by simultaneously acquiring locking status signals, load-bearing connection status signals, and real-time inertial measurement data, can simultaneously obtain the mechanical health status of the hook, the correctness of the connected object, and spatial motion information, achieving multi-dimensional safety information fusion perception. Utilizing attitude calculation combining Kalman filtering and online drift compensation, it can effectively filter out high-frequency vibration noise caused by strong winds and human movements in the field. Employing a three-level progressive risk analysis logic, it performs risk analysis according to the priority order of locking failure as the highest priority, connection failure as the second highest priority, and spatial position anomaly as the last judgment, solving the problem of being unable to distinguish risk levels and realizing a complete safety protection chain from mechanical health to correct connection and then to attitude safety. Therefore, the multi-sensor fusion method for three-dimensional attitude monitoring and early warning of aerial work hooks proposed in this invention can solve the problem of low accuracy in three-dimensional attitude monitoring and early warning of aerial work hooks. Attached Figure Description

[0012] Figure 1 This is a flowchart illustrating a multi-sensor fusion method for three-dimensional attitude monitoring and early warning of high-altitude operation hooks, provided in an embodiment of the present invention.

[0013] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0014] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0015] This application provides a multi-sensor fusion method for monitoring and early warning of the three-dimensional attitude of a high-altitude work hook. The executing entity of this multi-sensor fusion method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the multi-sensor fusion method for monitoring and early warning of the three-dimensional attitude of a high-altitude work hook can be executed by software or hardware installed on a terminal device or a server device. The software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0016] Reference Figure 1 The diagram shown is a flowchart illustrating a multi-sensor fusion method for three-dimensional attitude monitoring and early warning of a high-altitude work hook according to an embodiment of the present invention. In this embodiment, the multi-sensor fusion method for three-dimensional attitude monitoring and early warning of a high-altitude work hook includes:

[0017] S1. In response to the mechanical operation of the hook of the speed difference self-locking device, synchronously acquire the locking status signal of the hook, the load-bearing connection status signal and real-time inertial measurement data of the speed difference self-locking device.

[0018] In this embodiment of the invention, the speed-differential self-locking device is a mechanical device for preventing falls during high-altitude operations. It includes a housing, a reel, a braking ratchet mechanism, and upper and lower connecting hooks. The hooks are the components that connect the speed-differential self-locking device to the worker's safety belt or anchor point, and the hooks are equipped with rotatable locking tongues.

[0019] In detail, the mechanical operation of the hook of the speed-differential self-locking device refers to the synchronous start of the data acquisition process when the operator performs a physical opening and closing action on the hook of the speed-differential self-locking device. The linear Hall sensor installed at the end of the hook's locking tongue movement path, the capacitive sensing unit installed on the inner wall of the load-bearing hole, and the two inertial measurement units located near the locking tongue and inside the housing, respectively, are triggered by this mechanical action.

[0020] Furthermore, the responsive mechanical operation ensures that the sensor only operates when the hook is actually used, avoiding the power waste caused by continuous power supply. At the same time, it ensures the initial alignment of the locking status signal, the load-bearing connection status signal and the inertial measurement data in time, providing a synchronous time reference for multi-source data fusion.

[0021] In this embodiment of the invention, the locking status signal refers to an electrical signal characterizing whether the hook latch has reached the fully locked position, and this signal is generated by a sensor installed at the end of the latch's movement path. The load-bearing connection status signal refers to an electrical signal characterizing whether there is a metal load-bearing object inside the hook's load-bearing hole, and this signal is generated by a capacitive sensing unit installed on the inner wall of the load-bearing hole. Real-time inertial measurement data refers to acceleration and angular velocity data collected by the inertial measurement unit; the measurement data reflects the hook's motion state in space.

[0022] In this embodiment of the invention, the synchronous acquisition of the locking status signal of the hook of the speed difference self-locking device, the load-bearing connection status signal, and real-time inertial measurement data includes:

[0023] In response to the mechanical opening and closing action of the fast differential self-locking device hook, a linear Hall sensor installed at the end of the hook lock tongue's movement path is triggered to collect the analog displacement of the hook lock tongue relative to the preset locking endpoint;

[0024] The displacement analog quantity is converted into a digital locking state parameter and compared with a pre-stored fully locked reference value to generate a locking state signal characterizing whether the hook bolt has reached the fully locked position.

[0025] The capacitive sensing unit installed on the inner wall of the hook's load-bearing hole is activated synchronously to detect whether there is a metal load-bearing object in the load-bearing hole, and generates a load-bearing connection status signal that characterizes whether the hook is in effective contact with the load-bearing object based on the capacitance change of the capacitive sensing unit.

[0026] The system synchronously collects the first acceleration data and the first angular velocity data output by the first inertial measurement unit installed near the hook lock tongue, and the second acceleration data and the second angular velocity data output by the second inertial measurement unit installed inside the speed difference self-locking device housing;

[0027] The first acceleration data and the second acceleration data are differentially processed to generate relative acceleration data representing the hook relative to the speed difference self-locking device housing. The relative acceleration data is then spatiotemporally aligned with the first angular velocity data and the second angular velocity data to obtain real-time inertial measurement data.

[0028] In detail, when the operator opens and then releases the hook latch, the latch moves towards the locked position under the action of a spring. A linear Hall sensor detects the displacement of the latch relative to a preset locking endpoint. A linear Hall sensor is a sensor whose output signal is proportional to the magnetic flux passing through it. A small permanent magnet is installed at the end of the hook latch. When the latch moves, the distance between the permanent magnet and the linear Hall sensor changes, causing a linear change in the voltage output of the sensor. The preset locking endpoint refers to the voltage value corresponding to the nominal distance between the permanent magnet and the sensor when the latch is fully engaged in the hook lock groove. When the operator opens the hook latch, the permanent magnet moves away from the sensor, and the output voltage decreases; when the latch rebounds under the action of the spring, the permanent magnet moves closer to the sensor, and the output voltage increases. The main control chip continuously acquires this output voltage at a set sampling rate to obtain an analog displacement.

[0029] Specifically, the analog-to-digital converter inside the main control chip converts the analog displacement value into a 12-bit or 16-bit digital value. The fully locked reference value is pre-stored through a factory calibration process: when the hook is in the fully locked state, the output value of the analog-to-digital converter is read, a small hysteresis window is added, and then stored. If the current digitized locked state parameter is greater than or equal to the fully locked reference value minus the lower hysteresis limit, it is determined that the latch has reached the fully locked position, and a logic high level is output; otherwise, a logic low level is output. The purpose of the hysteresis window is to prevent signal jitter when the latch is in a critical position, which would cause frequent state jumps.

[0030] Furthermore, the capacitive sensing unit employs the self-capacitance detection principle. A metal electrode is embedded in the inner wall of the hook's load-bearing hole, and this electrode is connected to a capacitance-to-digital converter. When no metal object penetrates the load-bearing hole, the parasitic capacitance between the electrode and the surrounding environment is small, and the value output by the capacitance-to-digital converter is at a baseline level. When the metal load-bearing D-ring penetrates the load-bearing hole, a coupling capacitance is formed between the metal and the electrode, increasing the total capacitance value. The larger the contact area and the closer the distance between the metal D-ring and the electrode, the greater the increase in capacitance. After converting the capacitance value into a digital quantity, the capacitance-to-digital converter compares it with a pre-calibrated effective contact threshold. This threshold is obtained by multiplying the capacitance value measured when a standard metal D-ring is fully inserted into the load-bearing hole and a rated tensile force is applied by a coefficient. If the current digital capacitance value exceeds this threshold, a valid contact indicator is generated; otherwise, an invalid contact indicator is generated.

[0031] Furthermore, the first inertial measurement unit (IMU) is encapsulated on a small circuit board and fixed inside the housing near the hook latch with shock-absorbing adhesive. The second IMU is installed inside the main housing of the differential self-locking device. Both IMUs use the same model and configuration to ensure data consistency. Each IMU outputs triaxial acceleration and triaxial angular velocity data at a sampling rate of 200Hz. The acceleration data range is set to ±8g, and the angular velocity data range is set to ±500 degrees per second to accommodate the range of motion in high-altitude operations. Since the worker's body will generate overall motion during climbing, both the first and second IMUs will experience the same common-mode acceleration. Subtracting the two acceleration data axis by axis yields the relative acceleration of the hook relative to the housing, thus eliminating interference from the overall human motion. The specific calculation formula is: the X-axis component of the relative acceleration equals the X-axis component of the first acceleration minus the X-axis component of the second acceleration; the same applies to the Y and Z axes. For angular velocity data, since there is no relative rotation between the two inertial measurement units (IMUs), the first and second angular velocity data should theoretically be equal. Taking their average as the final angular velocity data can improve the signal-to-noise ratio. Regarding spatiotemporal alignment, the main control chip adds a high-precision timestamp to each data packet. Because there may be slight deviations in the sampling times of the first and second IMUs, a linear interpolation method is used for time alignment: using the timestamp of the first angular velocity data as a reference, for the data from the second IMU, the estimated reference time is calculated by interpolation based on the values ​​of its two preceding and following sampling points. The data from both IMUs are transformed to the same coordinate system using a pre-calibrated rotation matrix, which is obtained at the factory using a six-position calibration method.

[0032] Furthermore, when the mechanical opening and closing action of the hook is detected, the main control chip generates a synchronization pulse. This pulse simultaneously initiates data acquisition from the analog-to-digital converter, the capacitor-to-digital converter, and the inertial measurement unit, ensuring that the start times of the three types of signals are aligned. This avoids errors in subsequent attitude calculation and risk analysis caused by time misalignment. For example, if the time deviation between the latching state signal and the inertial measurement data exceeds one sampling period, it may lead to the erroneous identification of a normal attitude as an anomaly.

[0033] S2. Perform Kalman filtering on the real-time inertial measurement data, and perform online drift compensation on the filtered data to generate hook three-dimensional attitude data.

[0034] In this embodiment of the invention, relative acceleration data and aligned angular velocity data are used to construct real-time inertial measurement data. This data has eliminated common-mode interference from the overall human motion, providing a clean input for subsequent Kalman filtering.

[0035] In this embodiment of the invention, performing Kalman filtering on the real-time inertial measurement data includes:

[0036] Obtain the timestamps of the relative acceleration data, the first angular velocity data, and the second angular velocity data from the real-time inertial measurement data;

[0037] Using the timestamp of the first angular velocity data as a reference, the relative acceleration data and the second angular velocity data are resampled in time to generate a time-aligned relative acceleration sequence, a first angular velocity sequence, and a second angular velocity sequence;

[0038] The relative acceleration sequence, the first angular velocity sequence, and the second angular velocity sequence are transformed according to a unified spatial coordinate system. The data of the first inertial measurement unit installed near the hook lock tongue and the data of the second inertial measurement unit installed in the speed difference self-locking device housing are both transformed to the hook coordinate system to generate a spatially aligned multi-source data group.

[0039] Using the angular velocity data in the multi-source data set as the state prediction input of the Kalman filter, a state transition matrix is ​​constructed to generate the predicted attitude quaternion at the current moment.

[0040] The predicted attitude quaternion is converted into a predicted gravity vector, and the vector residual between the predicted gravity vector and the measured gravity vector in the relative acceleration data is calculated. The predicted attitude quaternion is then corrected based on the vector residual to generate the optimal attitude estimate for the current moment.

[0041] The attitude quaternion in the optimal attitude estimate is used as the initial attitude data after eliminating human body sway interference.

[0042] In detail, the main control chip contains a high-precision hardware timer with microsecond-level timing accuracy. Whenever the inertial measurement unit completes a data acquisition and triggers an interrupt, the main control chip immediately reads the current value of this timer as the timestamp of the data packet. The timestamp and data are stored together in a circular buffer. The timestamp of the first angular velocity data is chosen as the reference because the rate of change of angular velocity data is usually higher than that of acceleration data, requiring higher time synchronization accuracy. For each timestamp of the first angular velocity data, the timestamp sequence of the second angular velocity data is examined, and the two closest sampling points before and after that moment are found. The estimated value of the second angular velocity at that moment is calculated using a linear interpolation formula. Similarly, the same interpolation process is performed on the relative acceleration data. After resampling, all data are strictly aligned in time, forming three sequences of equal length.

[0043] Specifically, the origin of the hook coordinate system is defined at the geometric center of the hook, the X-axis points towards the hook opening, the Y-axis is perpendicular to the opening plane, and the Z-axis points upwards along the direction of gravity. A fixed rotational relationship exists between the installation angle of the first inertial measurement unit and the hook coordinate system, represented by a 3x3 rotation matrix. This rotation matrix is ​​obtained through precision tooling measurements during equipment assembly. Multiplying the acceleration vector output by the first inertial measurement unit by this rotation matrix yields the acceleration in the hook coordinate system. Similarly, the data from the second inertial measurement unit is also transformed to the hook coordinate system using its corresponding rotation matrix. After spatial alignment, all data are expressed in the same coordinate system, allowing for direct fusion calculations.

[0044] Furthermore, the state variables of the Kalman filter are chosen as attitude quaternions, which are four-dimensional unit vectors capable of representing three-dimensional rotation without singularity. The state transition matrix is ​​constructed based on the angular velocity data. Let the angular velocity component at the current moment be... The sampling period is Then the quaternion differential equation can be discretized as: ,in It is a skew-symmetric matrix composed of angular velocity components, whose elements are composed of angular velocity components. Filled according to specific rules, the matrix multiplied by a quaternion can equivalently represent the derivative effect of angular velocity with respect to the quaternion. Indicates the first The attitude quaternion at a discrete moment (i.e., the current moment) is a four-dimensional unit vector consisting of four real components, used to represent the three-dimensional rotational attitude of the hook relative to the direction of gravity at the current moment without singularity. Indicates the first The predicted attitude quaternion for each discrete time step (i.e., the next time step after one sampling period) is the attitude estimate for the next time step obtained recursively from the current attitude and angular velocity data. Using this recursive formula, the attitude quaternion for the current time step can be predicted from the optimal attitude quaternion for the previous time step. Simultaneously, the state prediction covariance matrix is ​​also updated according to the standard formula of Kalman filtering.

[0045] Furthermore, in the hook coordinate system, the theoretical direction of the gravity vector is downward along the Z-axis, and its magnitude is the standard gravitational acceleration. Applying the rotation represented by the predicted attitude quaternion to the theoretical gravity vector yields the component of the predicted gravity vector in the sensor coordinate system. The measured gravity vector extracted from the relative acceleration data is the result of filtering the accelerometer measurements to remove motion acceleration. Subtracting the predicted gravity vector from the measured gravity vector yields the three-dimensional vector residual. This residual reflects the error in attitude prediction. The Kalman gain matrix determines the weight of the residual on state correction; the corrected attitude quaternion equals the predicted attitude quaternion plus the Kalman gain multiplied by the residual. To ensure the unit magnitude of the quaternion, normalization is required after correction. The resulting optimal attitude estimate is the preliminary attitude data for the current moment. After Kalman filtering, the output is a four-dimensional unit quaternion. This quaternion has fused angular velocity data (predicted) and acceleration data (observed), and common-mode interference from the overall human motion has been eliminated through differential operations. Therefore, this attitude quaternion represents the true spatial attitude of the hook relative to the direction of gravity, and is not affected by the swaying of the operator's body.

[0046] Furthermore, using the relative acceleration after differential IMU analysis as the observation input effectively suppresses human body swaying interference. However, the initial attitude data may be unreliable, so correction processing is required.

[0047] In this embodiment of the invention, the hook three-dimensional attitude data refers to a set of data that describes the complete pointing information of the hook in space relative to the direction of gravity, obtained after Kalman filtering and online drift compensation. This data includes, but is not limited to, vertical tilt angle components and horizontal offset angle components.

[0048] In this embodiment of the invention, the step of performing online drift compensation on the filtered data to generate hook three-dimensional attitude data includes:

[0049] Extract the gravity acceleration vector from the preliminary attitude data, and extract the projection components of the gravity acceleration vector on the three orthogonal axes in the hook coordinate system to generate the three components of gravity projection.

[0050] The direction angle of the gravitational acceleration vector relative to each coordinate axis of the hook coordinate system is calculated based on the three components of the gravity projection, and the direction angle is compared with the mechanical limit angle set of the speed difference self-locking device hook, wherein the mechanical limit angle set includes the first limit angle when the lock tongue is fully locked and the second limit angle along the axis of the hook load-bearing hole.

[0051] If the deviation between the orientation angle and the first limiting angle or the second limiting angle exceeds a preset geometric compatibility threshold, a projection correction is applied to the three components of the gravity projection based on the mechanical limiting angle set to generate a corrected gravity vector that conforms to mechanical geometric constraints.

[0052] The modified gravity vector is transformed back into an attitude quaternion, and the attitude quaternion is normalized to generate hook three-dimensional attitude data.

[0053] In detail, the initial attitude data is an attitude quaternion that describes the rotational relationship between the hook coordinate system and the world coordinate system (gravity direction). By inversely rotating the gravity vector in the world coordinate system (downward along the Z-axis, with a magnitude of standard gravitational acceleration) to the hook coordinate system using this quaternion, we can obtain the three components of the gravitational acceleration vector in the hook coordinate system, denoted as follows: This refers to the three components of the gravity projection. The direction angle of the gravitational acceleration vector in the hook coordinate system can be calculated using the arctangent function. For example, the direction angle between the gravity vector and the X-axis. equal , direction angle with the Y-axis equal , direction angle with the Z-axis equal The set of mechanical limit angles are inherent geometric parameters of the speed-differential self-locking device. The first limit angle refers to the angle between the axis of the latch and the hook body when the latch is fully locked. This angle is determined by the mechanical design of the hook, for example, 90 degrees. The second limit angle refers to the direction of the central axis of the hook's load-bearing hole. The angle between this direction and the hook body is also fixed, for example, 0 degrees. These angles are pre-stored in the device's non-volatile memory.

[0054] Specifically, the geometric compatibility threshold is a small angle, such as 5 degrees, used to determine whether the current posture conforms to the allowable range of the mechanical structure. If the deviation between the calculated gravity direction angle and the first limit angle (the theoretical angle when the latch is locked) is greater than 5 degrees, it indicates that the posture calculation result contradicts physical reality, because the angle of the latch should be fixed once it is locked. In this case, it is necessary to forcibly correct the three components of the gravity projection to conform to the mechanical constraints, that is, to project the gravity vector onto the plane corresponding to the mechanical limit angle, and then recalculate the components that satisfy the constraints. For example, if the latch axis should be vertically upward when the latch is locked, then the projection of the gravity vector on this axis should be close to the standard gravitational acceleration. Through projection correction, a corrected gravity vector that satisfies the mechanical geometric constraints is generated.

[0055] Furthermore, given the components of a 3D vector in the hook coordinate system and its theoretical value in the world coordinate system (gravity vector), the rotational relationship between the two coordinate systems can be solved inversely. In addition, the corrected gravity vector provides the correct orientation of gravity in the hook coordinate system, which is equivalent to providing the hook's pitch and roll angles. Combined with the yaw angle output by the Kalman filter (which is unaffected by gravity constraints), a complete attitude quaternion can be reassembled. Finally, this quaternion is normalized to ensure its modulus is 1, yielding the final 3D attitude data of the hook, which, after gravity drift compensation and mechanical geometric constraint correction, possesses high reliability.

[0056] Furthermore, the mechanical structural characteristics of the speed difference self-locking device were used as a priori constraints to verify and correct the results of the pure inertial calculation, which significantly improved the physical consistency of the attitude data and provided a reliable data foundation for subsequent risk analysis.

[0057] S3. Based on the locking state signal, perform a first-level risk analysis on the three-dimensional attitude of the hook. If the analysis result meets the preset first risk condition, generate a first-priority early warning command representing the hook locking failure.

[0058] In this embodiment of the invention, the first-level risk analysis refers to the highest-priority risk assessment process centered on the hook locking status. This process prioritizes identifying fatal faults such as incomplete locking of the bolt and mechanical failure. The first-priority early warning command refers to the highest-level alarm signal generated when a hook locking fault is detected. This command triggers a local buzzer to sound continuously at the highest frequency and sends an emergency alarm data frame via wireless communication.

[0059] In this embodiment of the invention, the first-level risk analysis of the hook's three-dimensional attitude based on the locking state signal, and the generation of a first-priority early warning instruction characterizing a hook locking failure if the analysis result meets a preset first risk condition, includes:

[0060] The locking state signal is compared with a pre-stored fully locked benchmark feature library to determine whether the bolt displacement represented by the locking state signal is lower than the lower limit threshold of the displacement in the fully locked benchmark feature library.

[0061] When the displacement of the locking tongue is lower than the lower limit threshold, the first-level risk judgment flag is triggered, and the duration of the current locking state signal is read.

[0062] If the duration exceeds the preset fault confirmation time window, the risk level identifier register is set to the first priority state, and the current lockout state signal is latched as fault evidence data.

[0063] Based on the first priority status and the fault evidence data, a frequency drive signal for the local buzzer is generated, and a remote alarm data frame containing a device identifier and a fault type code is generated. The frequency drive signal and the remote alarm data frame are used as the first priority warning instruction to characterize the hook interlocking fault.

[0064] In detail, the fully locked reference feature library is established through multiple measurements at the time of equipment delivery. The library includes upper and lower displacement thresholds. The upper displacement threshold is the maximum value output by the sensor when the bolt is fully locked, and the lower displacement threshold is the minimum value output by the sensor when the bolt is fully open. During normal use, if the bolt fails to fully rebound, its displacement will fall between the lower and upper thresholds. Comparing the currently acquired locked state signal (digitized displacement) with the lower displacement threshold indicates that the bolt has not even reached the open position, which is an abnormal situation.

[0065] Specifically, to prevent misjudgments caused by transient noise, the system does not immediately trigger an alarm, but instead starts a timer. The first-level risk assessment flag is a software flag bit; setting it to 1 indicates that a potential locking fault has been detected. The system begins to accumulate the time the locking status signal remains in an abnormal state. The duration is obtained by reading the value of the hardware timer and subtracting the timestamp of the abnormal start time. The fault confirmation time window is a preset time constant, such as 200 milliseconds. This time window is selected based on the normal completion time of the mechanical locking action (usually less than 100 milliseconds) plus a certain redundancy. If the abnormal duration exceeds 200 milliseconds, it indicates that the bolt has indeed failed to lock correctly, rather than being a momentary jitter. At this time, the risk level identifier register is written with a value representing the first priority. Simultaneously, the current locking status signal and the data within a certain period before and after it are latched into the fault evidence data buffer for subsequent analysis and recording.

[0066] Furthermore, the first-priority warning command includes two output channels. Local output channel: The main control chip's PWM module generates a square wave signal of a specific frequency, such as a continuous pulse with a frequency of 4000Hz and a duty cycle of 50%, driving the buzzer to emit a rapid, high-frequency warning sound. Simultaneously, the control indicator light flashes red at the same frequency. Remote output channel: The main control chip encapsulates the device's unique identifier (e.g., a 32-bit serial number burned in at the factory) and fault type code (e.g., 0x01 indicating a hook locking fault) according to the LoRa wireless communication protocol frame format. The frame structure includes a preamble, device address, command word (0x81 indicating an emergency alarm), data length, fault type code, and CRC checksum. The data frame is transmitted at maximum power via the LoRa module, and upon receipt by the ground monitoring terminal, it immediately broadcasts a voice announcement and displays a message on the screen.

[0067] Furthermore, the first-level risk analysis prioritizes responding to mechanical failures of the hook itself, which can detect hidden faults such as spring aging and foreign object jamming before hooking, ensuring that the highest priority alarm is triggered only in a continuous fault state, thus guaranteeing safety and avoiding false triggering.

[0068] S4. Under the premise that the locking status signal indicates that the hook lock tongue is in a fully locked state, perform a second-level risk analysis on the three-dimensional attitude of the hook according to the load-bearing connection status signal. If the analysis result meets the preset second risk condition, generate a second priority warning instruction that characterizes the hook connection failure.

[0069] In this embodiment of the invention, the second-level risk analysis refers to the risk assessment process of determining whether the hook is effectively attached to a load-bearing object under the premise of normal locking. This process is used to identify failure states such as false attachment or attachment of non-load-bearing components.

[0070] In this embodiment of the invention, the step of performing a second-level risk analysis on the three-dimensional attitude of the hook based on the load-bearing connection status signal includes:

[0071] The sensed value in the load-bearing connection status signal is read, and the sensed value is compared with the pre-stored metal load-bearing object sensed characteristic threshold to generate a preliminary determination result of contact effectiveness;

[0072] When the preliminary determination of the contact effectiveness is invalid contact, the rate of change of the vertical tilt angle component and the horizontal offset angle component in the three-dimensional posture data of the hook is obtained, and the motion mode of the hook within a preset time window is analyzed based on the rate of change.

[0073] If the motion pattern is a fast swing followed by a decay of the hanging stability feature, a temporary contact mark is generated, and the preliminary determination result of the contact validity is corrected to a pending confirmation state. If the motion pattern is a continuous random swing or a stationary state without load-bearing features, the invalid contact determination is maintained.

[0074] When in the pending confirmation state, start the hook stabilization timer to continuously monitor the energy decay of the vertical acceleration component of the hook's three-dimensional attitude data, and determine the second-level risk analysis result based on the energy decay.

[0075] When the invalid contact determination is maintained, the second-level risk analysis result is output as there is a risk of attachment failure, and the current sensing value and attitude data are recorded as evidence of failure.

[0076] In detail, the induced value in the load-bearing connection status signal is the digital quantity output by the capacitor-to-digital converter. The induced characteristic threshold of the metal load-bearing object is obtained through calibration. This involves fully inserting a standard metal D-ring into the load-bearing hole and applying the rated load, recording the output value of the capacitor-to-digital converter, and multiplying this value by 0.8 to obtain the threshold (leaving a 20% margin). If the current induced value is greater than or equal to this threshold, it is preliminarily determined to be a valid contact; otherwise, it is an invalid contact.

[0077] Specifically, the rate of change of the vertical tilt component is calculated using differential methods. This rate of change equals the current vertical tilt angle minus the vertical tilt angle of the previous sampling point, divided by the sampling interval. The rate of change of the horizontal offset component is calculated similarly. A preset time window is typically 0.5 seconds, containing approximately 100 sampling points. Analyzing the statistical characteristics of the rate of change in these 100 points, such as root mean square, peak value, and zero-crossing rate, can determine the motion pattern. For example, a rapid swing followed by attenuation pattern is characterized by a large rate of change in the initial few frames, which then gradually decreases; continuous random swing is characterized by a rate of change fluctuating at a consistently high level; and a stationary but unloaded state is characterized by a rate of change close to zero. The hook-up stability characteristic refers to the process where, after the hook is attached to a load-bearing object, it experiences a brief swing before gradually stabilizing. If the motion pattern matches this characteristic, even if the capacitive sensing value is temporarily below the threshold (possibly due to incomplete contact or sensor sensitivity issues), it is considered a potentially valid hook-up, and the status is set to pending confirmation. If the movement pattern is continuous random swinging, it means the hook is in a free-swinging state and may not be hanging anything at all; if it is stationary but unloaded, it means the hook is hanging somewhere but not under force, such as on clothing. In both cases, the invalid contact determination remains.

[0078] Furthermore, the stabilization timer is set to a time window, for example, 2 seconds. During this period, the energy of the vertical acceleration component is continuously calculated, and the energy is defined as the integral of the square of the acceleration. When the hook is stably attached, the energy of the vertical acceleration will decay to near zero (only the gravitational component remains, with no dynamic acceleration). If the energy decays to below the preset stabilization energy threshold within this time window, the attachment is confirmed to be effective, and the second-level risk analysis result is no risk. If the energy remains above the threshold, it indicates that the hook is constantly swaying, and the attachment may be unstable or incomplete, at which point the output indicates a risk of attachment failure. If the motion pattern analysis result maintains the invalid contact judgment, the output directly indicates a risk of attachment failure. At the same time, the current capacitance sensing value, vertical tilt angle, horizontal offset angle, and other data are packaged and stored in the failure evidence buffer for subsequent generation of specific warning instructions to help distinguish different failure types.

[0079] Furthermore, the second risk analysis utilizes kinetic characteristics to assist in capacitance detection, improving the accuracy of determining the validity of the connection.

[0080] In this embodiment of the invention, the second priority warning instruction refers to the second-highest level alarm signal generated when a connection failure is detected. This instruction triggers a local buzzer to sound intermittently at a medium frequency and sends a warning data frame via wireless communication.

[0081] In this embodiment of the invention, generating a second priority early warning instruction characterizing hook connection failure if the analysis result meets a preset second risk condition includes:

[0082] When the second-level risk analysis result indicates that there is a risk of attachment failure, if the sensing value in the failure evidence is within the preset completely unloaded reference range, the failure type is determined to be no object attached; if the sensing value is within the partial contact reference range, the failure type is determined to be attached to a non-load-bearing object.

[0083] If the vertical tilt component of the attitude data in the failure evidence remains within a preset angle range and the horizontal offset component meets the preset change conditions, the failure subtype is determined to be a false hang accompanied by sway risk. If neither the vertical tilt component nor the horizontal offset component changes significantly, the failure subtype is determined to be a static false hang.

[0084] The failure type and the failure subtype are encoded as a hook failure type code, and the second priority internal sub-level of the current hook failure is determined according to the preset hook failure severity mapping table.

[0085] The hook failure type code, the second priority internal sub-level, and the failure evidence are encapsulated into a second priority warning instruction that characterizes hook failure.

[0086] In detail, the fully unloaded reference range is the capacitance value measured when there is no object inside the load-bearing hole, typically a small range, such as 100 to 150. The partial contact reference range is the capacitance value measured when a non-metallic object or a non-load-bearing metal object (such as a plastic buckle on clothing or a tool ring) passes through the load-bearing hole; this value lies between fully unloaded and effective contact, for example, 200 to 400. If the current capacitance value falls within the fully unloaded reference range, it indicates that there is no object inside the load-bearing hole, and the failure type is determined to be no object being attached. If it falls within the partial contact reference range, it indicates that an object has passed through but is not a metal load-bearing D-ring, and it is determined to be attached to a non-load-bearing object.

[0087] Specifically, the preset angle range can be a small range of vertical tilt angles less than 15 degrees. The horizontal offset angle component meeting the preset change condition means that the rate of change of the horizontal offset angle is consistently higher than a certain threshold, such as more than 20 degrees per second. If the vertical tilt angle remains small while the horizontal offset angle changes drastically, it indicates that the hook is attached to an object but is not stable, posing a risk of swaying; this is judged as a loose hook with a risk of swaying. If both the vertical tilt angle and the horizontal offset angle remain essentially unchanged, it indicates that the hook is stationary but not properly bearing weight, such as being hung on clothing in a fixed position; this is judged as a static loose hook.

[0088] Furthermore, the attachment failure type code is an 8-bit binary number. The high 4 bits represent the failure type (0001 indicates no object is attached, 0010 indicates attachment to a non-load-bearing object), and the low 4 bits represent the failure subtype (0001 indicates static false attachment, 0010 indicates false attachment accompanied by shaking risk). The attachment failure severity mapping table defines the internal sub-levels corresponding to different types: no object is attached, which is the most severe and is set as sub-level 1; attachment to a non-load-bearing object but static is sub-level 2; attachment to a non-load-bearing object and shaking is sub-level 3. The smaller the sub-level value, the higher the urgency. The data frame structure of the second priority warning command is as follows: frame header (0xAA55), device ID, command word (0x82 indicates attachment failure alarm), failure type code, internal sub-level, failure evidence data (including capacitance sensing value and attitude data), and CRC checksum. This command is sent to the ground monitoring terminal via the LoRa module. At the same time, the local buzzer emits a low-frequency, intermittent warning sound (e.g., 2000Hz, beeping for 0.1 seconds and stopping for 0.2 seconds), and the indicator light flashes yellow.

[0089] Furthermore, the second risk analysis enables a refined classification of attachment failures, providing ground monitoring personnel with detailed fault diagnosis information.

[0090] S5. Under the premise that the locking status signal indicates that the hook latch is in a fully locked state and the load-bearing connection status signal indicates that the hook is in an effective connection state, analyze the change characteristics of the hook's three-dimensional posture data, perform a third-level risk analysis on the hook's three-dimensional posture based on the change characteristics, and generate a third-priority early warning instruction that characterizes the abnormal spatial position of the hook anchor point if the analysis result meets the preset third risk conditions.

[0091] In this embodiment of the invention, the change feature refers to the feature composed of the relative fluctuation rate of vertical tilt angle, eccentricity feature, vertical tilt angle trend feature and main direction feature. This feature comprehensively describes the dynamic change characteristics of the hook's three-dimensional attitude data.

[0092] In this embodiment of the invention, analyzing the variation characteristics of the hook's three-dimensional posture data includes:

[0093] Extract the vertical time series of the vertical tilt angle component and the horizontal time series of the horizontal offset angle component from the hook's three-dimensional attitude data, and allocate independent sliding window buffers for the vertical time series and the horizontal time series respectively;

[0094] The short-term moving mean and short-term moving variance of the vertical tilt angle are calculated using the first window length in the sliding window buffer to generate the instantaneous fluctuation characteristics of the vertical tilt angle. The long-term moving mean of the vertical tilt angle is calculated using the second window length in the sliding window buffer to generate the trend characteristics of the vertical tilt angle.

[0095] Map each angle value in the horizontal time series to a direction vector on the unit circle, calculate the magnitude of the composite vector of the direction vectors, generate the clustering feature of the horizontal offset angle, and calculate the direction angle of the composite vector of the direction vectors to generate the main direction feature of the horizontal offset angle.

[0096] The vertical tilt instantaneous fluctuation feature is compared with the vertical tilt trend feature to generate the vertical tilt relative fluctuation rate. The aggregation feature is compared with the preset uniform distribution benchmark value to generate the eccentricity feature of the horizontal offset angle.

[0097] The vertical tilt relative volatility, the eccentricity feature, the vertical tilt trend feature, and the main direction feature are combined into a change feature vector, which serves as the change feature of the hook's three-dimensional attitude data.

[0098] In detail, the vertical tilt and horizontal offset components are scalars that change continuously over time. A circular buffer is allocated to each component, with a length of, for example, 100 sampling points, corresponding to 0.5 seconds of data. Each time new attitude data is acquired, the new vertical tilt value is pushed into the vertical time series buffer, while the oldest data is removed, ensuring the buffer always contains data from the most recent 0.5 seconds. The horizontal time series is handled similarly. The first window is relatively short, for example, using 10 sampling points (50 milliseconds). The short-term moving average is equal to the sum of these 10 points divided by 10, and the short-term moving variance is equal to the sum of the squares of the differences between each point and the mean divided by 10. The short-term moving average and variance reflect the instantaneous fluctuations in the vertical tilt. The second window is longer, for example, using 100 sampling points (0.5 seconds). The long-term moving average is equal to the sum of these 100 points divided by 100, reflecting the overall trend of the vertical tilt over a longer period. The combination of short and long windows can distinguish between instantaneous fluctuations and long-term trends.

[0099] Specifically, the horizontal offset angle is an angular value ranging from -180 degrees to 180 degrees. Mapping it to the unit circle as a direction vector, its X component is... The Y component is For N angle values ​​in a horizontal time series, the sum of all direction vectors is calculated to obtain a composite vector. The magnitude of the composite vector divided by N yields the clustering characteristic, which ranges from 0 to 1. A clustering value closer to 1 indicates that the horizontal offset angle is more concentrated in one direction; a clustering value closer to 0 indicates a more uniform angle distribution (random oscillation). The direction angle of the composite vector is calculated using the arctangent function, representing the dominant offset direction. The relative volatility of the vertical tilt angle is equal to the absolute value of the short-term sliding variance divided by the long-term sliding mean; this ratio reflects the strength of instantaneous volatility relative to the long-term trend. A small relative volatility indicates a gentle change in the vertical tilt angle, possibly indicating a static tilt; a large relative volatility indicates severe fluctuations. The uniform distribution baseline is a theoretical value; for example, for N points uniformly distributed on a circle, the expected magnitude of the composite vector is 0. Subtracting this baseline value from the calculated clustering value yields the eccentricity characteristic. An eccentricity greater than 0 indicates the presence of a dominant direction. The variation feature vector is a four-dimensional vector with four components: relative fluctuation of vertical tilt angle, eccentricity feature of horizontal offset angle, trend feature of vertical tilt angle, and principal direction feature of horizontal offset angle. This feature vector integrates dynamic information in both the vertical and horizontal directions.

[0100] In this embodiment of the invention, the third-level risk analysis refers to the risk analysis level that determines the rationality of the anchor point's spatial position based on the changing characteristics of the hook's three-dimensional attitude data, under the premise that the locking state is complete and the hooking is effective.

[0101] In this embodiment of the invention, the step of performing a third-level risk analysis on the three-dimensional attitude of the hook based on the changing characteristics includes:

[0102] If the vertical tilt trend feature in the change characteristics exceeds the preset low hanging risk trend threshold, it is determined that the hook has a risk tendency in the height direction.

[0103] If the relative volatility of the vertical tilt angle in the change characteristics is lower than the preset static volatility upper limit, the current hook is determined to be in a static tilt state.

[0104] If the eccentricity feature in the change characteristics exceeds the preset eccentricity threshold, it is determined that the hook has a stable horizontal offset.

[0105] The main directional feature in the change feature is matched with the preset danger direction interval. If the match is successful, it is determined that the hook is in a dangerous state in the horizontal direction.

[0106] The risk tendency in the vertical direction and the static tilt state are defined as vertical risk, the stable deviation in the horizontal direction and the dangerous state in the horizontal direction are defined as horizontal risk, and the vertical risk and the horizontal risk are combined into a composite directional risk.

[0107] In detail, the low-hang risk trend threshold is an angle value, such as 30 degrees. If the vertical tilt trend characteristic (long-term moving average) is greater than 30 degrees, it indicates that the vertical tilt angle of the hook is consistently too large, meaning the hook is hanging below the worker's waist, posing a low-hang risk. After this judgment, a risk tendency indicator in the height direction is generated. The static fluctuation upper limit is a dimensionless threshold, such as 0.05. If the relative volatility of the vertical tilt angle is less than 0.05, it indicates that the vertical tilt angle changes very gradually, and the hook is in a stable tilt state rather than a dynamic swinging state. This further confirms that the low-hang risk is a stable violation posture, rather than an instantaneous action. Combining the above two conditions, the low-hang risk can be reliably determined.

[0108] Specifically, for example, the eccentricity threshold is 0.7. If the eccentricity characteristic of the horizontal offset angle is greater than 0.7, it indicates that the horizontal offset angle is not randomly distributed but concentrated in a main direction, suggesting a stable horizontal offset of the hook relative to the worker, which may cause a pendulum risk. The danger direction range refers to those directions that may cause the worker to collide with the tower or other obstacles, such as the range of 30 to 90 degrees to the left and right sides relative to the front of the body. If the main direction characteristic falls within this range, it indicates that the eccentric direction is dangerous, and a horizontal danger state is determined to exist.

[0109] Furthermore, determining vertical risk requires meeting two conditions simultaneously: the vertical tilt trend exceeds a threshold and the relative volatility is below the static volatility upper limit. Determining horizontal risk requires simultaneously meeting the conditions that the eccentricity exceeds a threshold and the main direction falls into the danger zone. If only vertical risk exists, the output is vertical risk; if only horizontal risk exists, the output is horizontal risk; if both exist simultaneously, the output is composite directional risk. The output results will be used to generate corresponding early warning instructions, accurately distinguishing between single and composite risks.

[0110] In this embodiment of the invention, the third priority warning command refers to a conventional level alarm signal generated when an abnormality in the spatial position of the anchor point is detected. This command triggers a local buzzer to sound briefly once and sends a reminder data frame wirelessly.

[0111] In this embodiment of the invention, the step of generating a third-priority early warning instruction characterizing the abnormal spatial position of the hook anchor point if the analysis result meets the preset third risk condition includes:

[0112] If the output of the third-level risk analysis is vertical risk, the preset anomaly type code will be set to anchor point height anomaly.

[0113] If the output of the third-level risk analysis is horizontal risk, the anomaly type code is set as anchor point eccentricity anomaly;

[0114] If the output of the third-level risk analysis is a composite directional risk, the anomaly type code is set as anchor point composite anomaly;

[0115] The urgency sub-level corresponding to the output result of the third-level risk analysis and the anomaly type are encoded and encapsulated into an early warning instruction data packet. The current vertical tilt angle component and horizontal offset angle component are added to the early warning instruction data packet as supporting data. Based on the early warning instruction data packet and the supporting data, a third-priority early warning instruction characterizing the spatial position anomaly of the hook anchor point is generated.

[0116] In detail, the results of the Level 3 risk analysis may be vertical risk, horizontal risk, or a combination of both. When the output is vertical risk, an anomaly type code of 0x10 is written into the internal variable, representing an anomaly in anchor point height. This code will be used in the subsequently encapsulated warning instructions. When the output is horizontal risk, an anomaly type code of 0x11 is written, representing an anomaly in anchor point eccentricity. When the output is a combination of both risks, an anomaly type code of 0x12 is written, representing a combination of anomalies in anchor point height (both height and eccentricity issues exist simultaneously).

[0117] Specifically, the urgency sub-levels of the third-priority warning command are set according to the risk type: anchor point composite anomaly is sub-level 1 (most urgent), anchor point height anomaly is sub-level 2, and anchor point eccentricity anomaly is sub-level 3. The data frame structure includes a frame header (0xAA55), device ID, command word (0x83 indicates an anchor point spatial position anomaly alarm), anomaly type code, urgency sub-level, vertical tilt component (floating-point number), horizontal offset component (floating-point number), and CRC checksum. The local alarm method is a single short warning sound from a buzzer and a flashing blue indicator light. After receiving the command, the ground monitoring terminal will broadcast specific prompts such as "Equipment 01, anchor point height abnormal, please raise the anchor point" or "Equipment 01, anchor point eccentricity abnormal, please adjust to directly above," etc.

[0118] Furthermore, the refined risk analysis results are transformed into actionable alarm outputs, and specific location data is provided to assist operators in making corrective actions.

[0119] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0120] Therefore, the embodiments should be regarded as exemplary and non-limiting in all respects. The scope of the invention is not limited to the foregoing description, and all variations within the meaning and scope of equivalents falling within the protection scope are intended to be included in the invention.

[0121] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0122] Furthermore, it is clear that the word "including" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or systems described can also be implemented by a single unit or system through software or hardware. Terms such as "first," "second," etc., are used to indicate names and do not indicate any specific order.

[0123] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for three-dimensional attitude monitoring and early warning of aerial work hooks using multi-sensor fusion, characterized in that, The method includes: In response to the mechanical operation of the hook of the speed-differential self-locking device, the locking status signal, load-bearing connection status signal and real-time inertial measurement data of the hook of the speed-differential self-locking device are acquired simultaneously. The real-time inertial measurement data is subjected to Kalman filtering, and the filtered data is compensated for drift online to generate three-dimensional attitude data of the hook. Based on the locking state signal, a first-level risk analysis is performed on the three-dimensional attitude of the hook. If the analysis result meets the preset first risk condition, a first-priority early warning instruction representing the hook locking failure is generated. Under the premise that the locking status signal indicates that the hook lock tongue is in a fully locked state, a second-level risk analysis is performed on the three-dimensional attitude of the hook according to the load-bearing connection status signal. If the analysis result meets the preset second risk condition, a second priority warning instruction characterizing the hook connection failure is generated. Under the premise that the locking status signal indicates that the hook latch is in a fully locked state and the load-bearing connection status signal indicates that the hook is in a valid connection state, the change characteristics of the hook's three-dimensional attitude data are analyzed, and a third-level risk analysis of the hook's three-dimensional attitude is performed based on the change characteristics. If the analysis result meets the preset third risk conditions, a third-priority early warning instruction characterizing the abnormal spatial position of the hook anchor point is generated.

2. The method for three-dimensional attitude monitoring and early warning of high-altitude operation hooks using multi-sensor fusion as described in claim 1, characterized in that, The synchronous acquisition of the locking status signal, load-bearing connection status signal, and real-time inertial measurement data of the hook of the speed difference self-locking device includes: In response to the mechanical opening and closing action of the fast differential self-locking device hook, a linear Hall sensor installed at the end of the hook lock tongue's movement path is triggered to collect the analog displacement of the hook lock tongue relative to the preset locking endpoint; The displacement analog quantity is converted into a digital locking state parameter and compared with a pre-stored fully locked reference value to generate a locking state signal characterizing whether the hook bolt has reached the fully locked position. The capacitive sensing unit installed on the inner wall of the hook's load-bearing hole is activated synchronously to detect whether there is a metal load-bearing object in the load-bearing hole, and generates a load-bearing connection status signal that characterizes whether the hook is in effective contact with the load-bearing object based on the capacitance change of the capacitive sensing unit. The system synchronously collects the first acceleration data and the first angular velocity data output by the first inertial measurement unit installed near the hook lock tongue, and the second acceleration data and the second angular velocity data output by the second inertial measurement unit installed inside the speed difference self-locking device housing; The first acceleration data and the second acceleration data are differentially processed to generate relative acceleration data representing the hook relative to the speed difference self-locking device housing. The relative acceleration data is then spatiotemporally aligned with the first angular velocity data and the second angular velocity data to obtain real-time inertial measurement data.

3. The method for three-dimensional attitude monitoring and early warning of high-altitude operation hooks using multi-sensor fusion as described in claim 2, characterized in that, The Kalman filtering of the real-time inertial measurement data includes: Obtain the timestamps of the relative acceleration data, the first angular velocity data, and the second angular velocity data from the real-time inertial measurement data; Using the timestamp of the first angular velocity data as a reference, the relative acceleration data and the second angular velocity data are resampled in time to generate a time-aligned relative acceleration sequence, a first angular velocity sequence, and a second angular velocity sequence; The relative acceleration sequence, the first angular velocity sequence, and the second angular velocity sequence are transformed according to a unified spatial coordinate system. The data of the first inertial measurement unit installed near the hook lock tongue and the data of the second inertial measurement unit installed in the speed difference self-locking device housing are both transformed to the hook coordinate system to generate a spatially aligned multi-source data group. Using the angular velocity data in the multi-source data set as the state prediction input of the Kalman filter, a state transition matrix is ​​constructed to generate the predicted attitude quaternion at the current moment. The predicted attitude quaternion is converted into a predicted gravity vector, and the vector residual between the predicted gravity vector and the measured gravity vector in the relative acceleration data is calculated. The predicted attitude quaternion is then corrected based on the vector residual to generate the optimal attitude estimate for the current moment. The attitude quaternion in the optimal attitude estimate is used as the initial attitude data after eliminating human body sway interference.

4. The method for three-dimensional attitude monitoring and early warning of high-altitude operation hooks using multi-sensor fusion as described in claim 3, characterized in that, The process of performing online drift compensation on the filtered data to generate hook three-dimensional attitude data includes: Extract the gravity acceleration vector from the preliminary attitude data, and extract the projection components of the gravity acceleration vector on the three orthogonal axes in the hook coordinate system to generate the three components of gravity projection. The direction angle of the gravitational acceleration vector relative to each coordinate axis of the hook coordinate system is calculated based on the three components of the gravity projection, and the direction angle is compared with the mechanical limit angle set of the speed difference self-locking device hook, wherein the mechanical limit angle set includes the first limit angle when the lock tongue is fully locked and the second limit angle along the axis of the hook load-bearing hole. If the deviation between the orientation angle and the first limiting angle or the second limiting angle exceeds a preset geometric compatibility threshold, a projection correction is applied to the three components of the gravity projection based on the mechanical limiting angle set to generate a corrected gravity vector that conforms to mechanical geometric constraints. The modified gravity vector is transformed back into an attitude quaternion, and the attitude quaternion is normalized to generate hook three-dimensional attitude data.

5. The method for three-dimensional attitude monitoring and early warning of high-altitude operation hooks using multi-sensor fusion as described in claim 1, characterized in that, The first-level risk analysis of the hook's three-dimensional attitude based on the locking state signal is performed. If the analysis result meets the preset first risk condition, a first-priority early warning instruction characterizing the hook locking failure is generated, including: The locking state signal is compared with a pre-stored fully locked benchmark feature library to determine whether the bolt displacement represented by the locking state signal is lower than the lower limit threshold of the displacement in the fully locked benchmark feature library. When the displacement of the locking tongue is lower than the lower limit threshold, the first-level risk judgment flag is triggered, and the duration of the current locking state signal is read. If the duration exceeds the preset fault confirmation time window, the risk level identifier register is set to the first priority state, and the current lockout state signal is latched as fault evidence data. Based on the first priority status and the fault evidence data, a frequency drive signal for the local buzzer is generated, and a remote alarm data frame containing a device identifier and a fault type code is generated. The frequency drive signal and the remote alarm data frame are used as the first priority warning instruction to characterize the hook interlocking fault.

6. The method for three-dimensional attitude monitoring and early warning of high-altitude operation hooks using multi-sensor fusion as described in claim 1, characterized in that, The second-level risk analysis of the hook's three-dimensional attitude based on the load-bearing connection status signal includes: The sensed value in the load-bearing connection status signal is read, and the sensed value is compared with the pre-stored metal load-bearing object sensed characteristic threshold to generate a preliminary determination result of contact effectiveness; When the preliminary determination of the contact effectiveness is invalid contact, the rate of change of the vertical tilt angle component and the horizontal offset angle component in the three-dimensional posture data of the hook is obtained, and the motion mode of the hook within a preset time window is analyzed based on the rate of change. If the motion pattern is a fast swing followed by a decay of the hanging stability feature, a temporary contact mark is generated, and the preliminary determination result of the contact validity is corrected to a pending confirmation state. If the motion pattern is a continuous random swing or a stationary state without load-bearing features, the invalid contact determination is maintained. When in the pending confirmation state, start the hook stabilization timer to continuously monitor the energy decay of the vertical acceleration component of the hook's three-dimensional attitude data, and determine the second-level risk analysis result based on the energy decay. When the invalid contact determination is maintained, the second-level risk analysis result is output as there is a risk of attachment failure, and the current sensing value and attitude data are recorded as evidence of failure.

7. The method for three-dimensional attitude monitoring and early warning of high-altitude operation hooks using multi-sensor fusion as described in claim 6, characterized in that, If the analysis results meet the preset second risk conditions, a second priority early warning instruction representing hook connection failure is generated, including: When the second-level risk analysis result indicates that there is a risk of attachment failure, if the sensing value in the failure evidence is within the preset completely unloaded reference range, the failure type is determined to be no object attached; if the sensing value is within the partial contact reference range, the failure type is determined to be attached to a non-load-bearing object. If the vertical tilt component of the attitude data in the failure evidence remains within a preset angle range and the horizontal offset component meets the preset change conditions, the failure subtype is determined to be a false hang accompanied by sway risk. If neither the vertical tilt component nor the horizontal offset component changes significantly, the failure subtype is determined to be a static false hang. The failure type and the failure subtype are encoded as a hook failure type code, and the second priority internal sub-level of the current hook failure is determined according to the preset hook failure severity mapping table. The hook failure type code, the second priority internal sub-level, and the failure evidence are encapsulated into a second priority warning instruction that characterizes hook failure.

8. The method for three-dimensional attitude monitoring and early warning of high-altitude operation hooks using multi-sensor fusion as described in claim 1, characterized in that, The analysis of the variation characteristics of the hook's three-dimensional attitude data includes: Extract the vertical time series of the vertical tilt angle component and the horizontal time series of the horizontal offset angle component from the hook's three-dimensional attitude data, and allocate independent sliding window buffers for the vertical time series and the horizontal time series respectively; The short-term moving mean and short-term moving variance of the vertical tilt angle are calculated using the first window length in the sliding window buffer to generate the instantaneous fluctuation characteristics of the vertical tilt angle. The long-term moving mean of the vertical tilt angle is calculated using the second window length in the sliding window buffer to generate the trend characteristics of the vertical tilt angle. Map each angle value in the horizontal time series to a direction vector on the unit circle, calculate the magnitude of the composite vector of the direction vectors, generate the clustering feature of the horizontal offset angle, and calculate the direction angle of the composite vector of the direction vectors to generate the main direction feature of the horizontal offset angle. The vertical tilt instantaneous fluctuation feature is compared with the vertical tilt trend feature to generate the vertical tilt relative fluctuation rate. The aggregation feature is compared with the preset uniform distribution benchmark value to generate the eccentricity feature of the horizontal offset angle. The vertical tilt relative volatility, the eccentricity feature, the vertical tilt trend feature, and the main direction feature are combined into a change feature vector, which serves as the change feature of the hook's three-dimensional attitude data.

9. The method for three-dimensional attitude monitoring and early warning of high-altitude operation hooks using multi-sensor fusion as described in claim 1, characterized in that, The third-level risk analysis of the hook's three-dimensional attitude based on the aforementioned change characteristics includes: If the vertical tilt trend feature in the change characteristics exceeds the preset low hanging risk trend threshold, it is determined that the hook has a risk tendency in the height direction. If the relative volatility of the vertical tilt angle in the change characteristics is lower than the preset static volatility upper limit, the current hook is determined to be in a static tilt state. If the eccentricity feature in the change characteristics exceeds the preset eccentricity threshold, it is determined that the hook has a stable horizontal offset. The main directional feature in the change feature is matched with the preset danger direction interval. If the match is successful, it is determined that the hook is in a dangerous state in the horizontal direction. The risk tendency in the vertical direction and the static tilt state are defined as vertical risk, the stable deviation in the horizontal direction and the dangerous state in the horizontal direction are defined as horizontal risk, and the vertical risk and the horizontal risk are combined into a composite directional risk.

10. The method for three-dimensional attitude monitoring and early warning of high-altitude operation hooks using multi-sensor fusion as described in claim 1, characterized in that, If the analysis results meet the preset third risk condition, a third priority early warning instruction is generated to characterize the abnormal spatial position of the hook anchor point, including: If the output of the third-level risk analysis is vertical risk, the preset anomaly type code will be set to anchor point height anomaly. If the output of the third-level risk analysis is horizontal risk, the anomaly type code is set as anchor point eccentricity anomaly; If the output of the third-level risk analysis is a composite directional risk, the anomaly type code is set as anchor point composite anomaly; The urgency sub-level corresponding to the output result of the third-level risk analysis and the anomaly type are encoded and encapsulated into an early warning instruction data packet. The current vertical tilt angle component and horizontal offset angle component are added to the early warning instruction data packet as supporting data. Based on the early warning instruction data packet and the supporting data, a third-priority early warning instruction characterizing the spatial position anomaly of the hook anchor point is generated.

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